


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser
def 							UpperCamelCase_(					_A :Union[str, Any]=None					,  _A :List[str]=None	)->		int:
			return field(default_factory=lambda: default					,  metadata=_A	)







@dataclass
class 							lowerCamelCase__						:






			"""simple docstring"""






			_UpperCamelCase		:       str 			=		field(
			    metadata={'help': 'The csv file to plot.'}       ,						)
			_UpperCamelCase		:       bool 			=		field(
			    default=UpperCAmelCase       ,						metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}       ,						)
			_UpperCamelCase		:       bool 			=		field(
			    default=UpperCAmelCase       ,						metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}       ,						)
			_UpperCamelCase		:       bool 			=		field(
			    default=UpperCAmelCase       ,						metadata={'help': 'Disable logarithmic scale when plotting'}       ,						)
			_UpperCamelCase		:       bool 			=		field(
			    default=UpperCAmelCase       ,						metadata={
			        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
			    }       ,						)
			_UpperCamelCase		:       Optional[str] 			=		field(
			    default=UpperCAmelCase       ,						metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}       ,						)
			_UpperCamelCase		:       Optional[List[str]] 			=		list_field(
			    default=UpperCAmelCase       ,						metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}   )



def 							UpperCamelCase_(					_A :Tuple	)->		str:
			try:
						int(_A	)
						return True
			except ValueError:
						return False
def 							UpperCamelCase_(					_A :Union[str, Any]	)->		Optional[Any]:
			try:
						float(_A	)
						return True
			except ValueError:
						return False







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							args
						UpperCamelCase__       =							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

						with open(self.args.csv_file   ,    newline=""  ) as csv_file:
									UpperCamelCase__       =							csv.DictReader(snake_case  )
									for row in reader:
												UpperCamelCase__       =							row["model"]
												self.result_dict[model_name]["bsz"].append(int(row["batch_size"]  )  )
												self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]  )  )
												if can_convert_to_int(row["result"]  ):
															# value is not None
															UpperCamelCase__       =							int(row["result"]  )
												elif can_convert_to_float(row["result"]  ):
															# value is not None
															UpperCamelCase__       =							float(row["result"]  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							plt.subplots()
						UpperCamelCase__       =							"Time usage" if self.args.is_time else "Memory usage"
						UpperCamelCase__       =							title_str + " for training" if self.args.is_train else title_str + " for inference"

						if not self.args.no_log_scale:
									# set logarithm scales
									ax.set_xscale("log"  )
									ax.set_yscale("log"  )

						for axis in [ax.xaxis, ax.yaxis]:
									axis.set_major_formatter(ScalarFormatter()  )

						for model_name_idx, model_name in enumerate(self.result_dict.keys()  ):
									UpperCamelCase__       =							sorted(set(self.result_dict[model_name]["bsz"]  )  )
									UpperCamelCase__       =							sorted(set(self.result_dict[model_name]["seq_len"]  )  )
									UpperCamelCase__       =							self.result_dict[model_name]["result"]

									((UpperCamelCase__),   (UpperCamelCase__))       =							(
									    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
									)

									UpperCamelCase__       =							(
									    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
									)

									for inner_loop_value in inner_loop_array:
												if self.args.plot_along_batch:
															UpperCamelCase__       =							np.asarray(
															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,    dtype=snake_case   ,    )
												else:
															UpperCamelCase__       =							np.asarray(
															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,    dtype=np.floataa   ,    )

												((UpperCamelCase__),   (UpperCamelCase__))       =							(
												    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
												)

												UpperCamelCase__       =							np.asarray(snake_case   ,    snake_case  )[: len(snake_case  )]
												plt.scatter(
												    snake_case   ,    snake_case   ,    label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''  )
												plt.plot(snake_case   ,    snake_case   ,    "--"  )

									title_str += F''' {label_model_name} vs.'''

						UpperCamelCase__       =							title_str[:-4]
						UpperCamelCase__       =							"Time in s" if self.args.is_time else "Memory in MB"

						# plot
						plt.title(snake_case  )
						plt.xlabel(snake_case  )
						plt.ylabel(snake_case  )
						plt.legend()

						if self.args.figure_png_file is not None:
									plt.savefig(self.args.figure_png_file  )
						else:
									plt.show()



def 							UpperCamelCase_(					)->		str:
			UpperCamelCase__       =							HfArgumentParser(_A	)
			UpperCamelCase__       =							parser.parse_args_into_dataclasses()[0]
			UpperCamelCase__       =							Plot(args=_A	)
			plot.plot()


if __name__ == "__main__":
						main()






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       int 			=		(DDIMParallelScheduler,)
			_UpperCamelCase		:       List[Any] 			=		(('eta', 0.0), ('num_inference_steps', 50))




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							{
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "clip_sample": True,
						}

						config.update(**snake_case  )
						return config




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(**snake_case  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter

						scheduler.set_timesteps(snake_case  )

						for t in scheduler.timesteps:
									UpperCamelCase__       =							model(snake_case   ,    snake_case  )
									UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						return sample




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timesteps in [100, 500, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=snake_case  )

						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(steps_offset=1  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )
						scheduler.set_timesteps(5  )
						assert torch.equal(scheduler.timesteps   ,    torch.LongTensor([801, 601, 401, 201, 1]  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,    [0.002, 0.02, 0.2, 2]  ):
									self.check_over_configs(beta_start=snake_case   ,    beta_end=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.check_over_configs(thresholding=snake_case  )
						for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(
												    thresholding=snake_case   ,    prediction_type=snake_case   ,    sample_max_value=snake_case   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t in [1, 10, 49]:
									self.check_over_forward(time_step=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, num_inference_steps in zip([1, 10, 50]   ,    [10, 50, 500]  ):
									self.check_over_forward(time_step=snake_case   ,    num_inference_steps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, eta in zip([1, 10, 49]   ,    [0.0, 0.5, 1.0]  ):
									self.check_over_forward(time_step=snake_case   ,    eta=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420   ,    400  ) - 0.14771  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980   ,    960  ) - 0.32460  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487   ,    486  ) - 0.00979  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999   ,    998  ) - 0.02  )  ) < 1E-5




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0
						scheduler.set_timesteps(snake_case  )

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter
						UpperCamelCase__       =							self.dummy_sample_deter + 0.1
						UpperCamelCase__       =							self.dummy_sample_deter - 0.1

						UpperCamelCase__       =							samplea.shape[0]
						UpperCamelCase__       =							torch.stack([samplea, samplea, samplea]   ,    dim=0  )
						UpperCamelCase__       =							torch.arange(snake_case  )[0:3, None].repeat(1   ,    snake_case  )

						UpperCamelCase__       =							model(samples.flatten(0   ,    1  )   ,    timesteps.flatten(0   ,    1  )  )
						UpperCamelCase__       =							scheduler.batch_step_no_noise(snake_case   ,    timesteps.flatten(0   ,    1  )   ,    samples.flatten(0   ,    1  )   ,    snake_case  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 1147.7904  ) < 1E-2
						assert abs(result_mean.item() - 0.4982  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop()

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 172.0067  ) < 1E-2
						assert abs(result_mean.item() - 0.223967  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(prediction_type="v_prediction"  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 52.5302  ) < 1E-2
						assert abs(result_mean.item() - 0.0684  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.8295  ) < 1E-2
						assert abs(result_mean.item() - 0.1951  ) < 1E-3







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.0784  ) < 1E-2
						assert abs(result_mean.item() - 0.1941  ) < 1E-3


